Virus infection of honey bee queens alters lipid profiles and indirectly suppresses a retinue pheromone component via reducing ovary mass
Bibliographic record
Abstract
Summary Virus infections reduce honey bee ( Apis mellifera ) ovary mass (in part due to resource-allocation trade-offs with immunity) and are linked to the presence of supersedure cells in colonies, indicating that workers are attempting to replace their queen. Pheromones, lipids, and lipid transport proteins could mediate relationships among virus infection, ovary mass, and supersedure. When we infected honey bee queens in the laboratory and profiled their queen retinue pheromone (QRP) components from head extracts, we found that virus infections specifically reduced the QRP component methyl oleate. Data from an observational field study were consistent with this pattern. Lipidomics analysis of the same extracts suggests that virus infection decreases triacylglycerol abundances (major sources of stored energy). Reducing ovary mass via laying restriction was sufficient to lower methyl oleate abundance ― suggesting that virus infection reduces methyl oleate indirectly via ovary effects ― but was insufficient to reduce abundance of most triacylglycerols or stimulate immune effector expression. Abundance of circulating apolipophorin-III, a lipid transporter and putative potentiator of immune effectors, was lower in queens with restricted laying, suggesting that its expression may be controlled by nutrient availability while its immune-stimulating capacity is governed by other mechanisms. Prior research has shown that queen pheromone blends lacking methyl oleate are less attractive to workers; therefore, diminishing methyl oleate could result in a less desirable pheromone bouquet and possibly stimulate supersedure. The mechanism of methyl oleate reduction is yet to be determined, but is clearly tied to ovary size and possibly resource availability or mobilization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".